# source: https://raw.githubusercontent.com/sisxmax/Freqtrade-Strategy-Library/e1315c78f00c4d813217ced1e7bc9f8364d3a06d/MultiWmaMacd.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake13: noqa: F401 # isort: skip_file # --- Do not remove these imports --- import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Dict, Optional, Union, Tuple import logging logger = logging.getLogger(__name__) from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, # @informative decorator # Hyperopt Parameters BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, # timeframe helpers timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, # Strategy helper functions merge_informative_pair, stoploss_from_absolute, stoploss_from_open, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta from technical import qtpylib # ========================================== # 13-21-34 WMA & MACD Strategy made 293% Profit! (Full Tutorial) # YouTube Link: https://youtu.be/Rm8bcBPKoJA # ========================================== # ================================ # Freqtrade Version # ================================ """ freqtrade -V Operating System: Linux-6.10.14-linuxkit-aarch64-with-glibc2.36 Python Version: Python 3.13.5 CCXT Version: 4.4.96 Freqtrade Version: freqtrade 2025.7 """ # ================================ # Download Historical Data # ================================ """ freqtrade download-data \ -c user_data/binance_futures_Github_sisxmax_Freqtrade_Strategy_Library__MultiWmaMacd__20260316_095247.json \ --timerange 20230101- \ -t 1m 5m 15m 30m 1h 4h 1d """ # ================================ # Hyperopt Optimization # ================================ """ freqtrade hyperopt \ --strategy Github_sisxmax_Freqtrade_Strategy_Library__MultiWmaMacd__20260316_095247 \ --config user_data/binance_futures_Github_sisxmax_Freqtrade_Strategy_Library__MultiWmaMacd__20260316_095247.json \ --timeframe 1h \ --timerange 20240801-20250401 \ --hyperopt-loss MultiMetricHyperOptLoss \ --spaces buy\ -e 50 \ --j -2 \ --random-state 9319 \ --min-trades 30 \ --max-open-trades 1 \ -p ICP/USDT:USDT """ # ================================ # Backtesting # ================================ """ freqtrade backtesting \ --strategy Github_sisxmax_Freqtrade_Strategy_Library__MultiWmaMacd__20260316_095247 \ --timeframe 1h \ --timerange 20240801-20250801 \ --breakdown month \ -c user_data/binance_futures_Github_sisxmax_Freqtrade_Strategy_Library__MultiWmaMacd__20260316_095247.json \ --max-open-trades 1 \ --cache none \ --timeframe-detail 5m \ -p ICP/USDT:USDT """ # ================================ # Start FreqUI Web Interface # ================================ """ freqtrade webserver \ --config user_data/binance_futures_Github_sisxmax_Freqtrade_Strategy_Library__MultiWmaMacd__20260316_095247.json """ class Github_sisxmax_Freqtrade_Strategy_Library__MultiWmaMacd__20260316_095247(IStrategy): # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 3 # Optimal timeframe for the strategy. timeframe = "1h" # Can this strategy go short? can_short: bool = True # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = {} # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.25 # Trailing stoploss trailing_stop = False # trailing_only_offset_is_reached = False # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # Strategy parameters wma_rolling_window = CategoricalParameter([4, 6], default=4, space="buy") macd_rolling_window = CategoricalParameter([13, 10], default=13, space="buy") risk_ratio = CategoricalParameter([2, 2.5, 3], default=2, space="buy") atr_mult = CategoricalParameter([2, 2.5, 3], default=2.5, space="buy") leverage_level = IntParameter(1, 10, default=1, space="buy", optimize=False, load=False) @property def plot_config(self): return { "main_plot": { "wma13": {"color": "red"}, "wma21": {"color": "blue"}, "wma34": {"color": "green"}, }, "subplots": { "MACD": { "macd": {"color": "#2962ff", "fill_to": "macdhist"}, "macdsignal": {"color": "#ff6d00"}, "macdhist": {"type": "bar", "plotly": {"opacity": 0.9}} } } } def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["wma13"] = ta.WMA(dataframe, timeperiod=13) dataframe["wma21"] = ta.WMA(dataframe, timeperiod=21) dataframe["wma34"] = ta.WMA(dataframe, timeperiod=34) macd = ta.MACD(dataframe) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"] dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe["wma13"] > dataframe["wma21"]) & (dataframe["wma21"] > dataframe["wma34"]) & # Check if WMA(13) crossed above WMA(34) in the rolling window (dataframe["wma13"].rolling(window=self.wma_rolling_window.value).apply( lambda x: any(qtpylib.crossed_above(x, dataframe["wma34"].iloc[x.index[0]:x.index[-1]+1])))) & # Check if WMA(21) crossed above WMA(34) in the rolling window (dataframe["wma21"].rolling(window=self.wma_rolling_window.value).apply( lambda x: any(qtpylib.crossed_above(x, dataframe["wma34"].iloc[x.index[0]:x.index[-1]+1])))) & (dataframe["macd"] > dataframe["macdsignal"]) & # Check if MACD crossed above MACD signal in the rolling window (dataframe["macd"].rolling(window=self.macd_rolling_window.value).apply( lambda x: any(qtpylib.crossed_above(x, dataframe["macdsignal"].iloc[x.index[0]:x.index[-1]+1])) )) & (dataframe["volume"] > 0) ), "enter_long"] = 1 dataframe.loc[ ( (dataframe["wma13"] < dataframe["wma21"]) & (dataframe["wma21"] < dataframe["wma34"]) & # Check if WMA(13) crossed below WMA(21) in the rolling window (dataframe["wma13"].rolling(window=self.wma_rolling_window.value).apply( lambda x: any(qtpylib.crossed_below(x, dataframe["wma34"].iloc[x.index[0]:x.index[-1]+1])))) & # Check if WMA(21) crossed below WMA(34) in the rolling window (dataframe["wma21"].rolling(window=self.wma_rolling_window.value).apply( lambda x: any(qtpylib.crossed_below(x, dataframe["wma34"].iloc[x.index[0]:x.index[-1]+1])))) & (dataframe['macd'] < dataframe['macdsignal']) & # Check if MACD crossed below MACD signal in the rolling window (dataframe['macd'].rolling(window=self.macd_rolling_window.value).apply( lambda x: any(qtpylib.crossed_below(x, dataframe['macdsignal'].iloc[x.index[0]:x.index[-1]+1])) )) & (dataframe["volume"] > 0) ), "enter_short"] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, "exit_long"] = 0 dataframe.loc[:, "exit_short"] = 0 return dataframe def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): side = -1 if trade.is_short else 1 # Retrieve TP and SL from custom data take_profit = trade.get_custom_data('take_profit') stop_loss = trade.get_custom_data('stop_loss') # If TP or SL is not set, initialize them if take_profit is None or stop_loss is None: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # Get the date just before trade opened trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) # Filter dataframe to candles before the trade opened signal_data = dataframe.loc[dataframe["date"] < trade_date] if signal_data.empty: logger.warning(f"[{pair}] No signal candle found. Skip setting TP/SL.") return None signal_candle = signal_data.iloc[-1] # Calculate TP and SL atr = signal_candle["atr"] close = signal_candle["close"] take_profit = close + side * self.atr_mult.value * atr * self.risk_ratio.value stop_loss = close - side * self.atr_mult.value * atr # Save to trade's custom data trade.set_custom_data('take_profit', take_profit) trade.set_custom_data('stop_loss', stop_loss) # logger.info(f"[{pair}] TP/SL set. TP: {take_profit:.2f}, SL: {stop_loss:.2f}") # Check exit conditions if (trade.is_short and current_rate <= take_profit) or \ (not trade.is_short and current_rate >= take_profit): # logger.info(f"[{pair}] Take Profit hit! Close: {current_close:.2f}, TP: {take_profit:.2f}") return "take_profit_achieved" if (trade.is_short and current_rate >= stop_loss) or \ (not trade.is_short and current_rate <= stop_loss): # logger.info(f"[{pair}] Stop Loss hit! Close: {current_close:.2f}, SL: {stop_loss:.2f}") return "stop_loss_achieved" return None def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float: return self.leverage_level.value